Greener Than Thou: People who protect the environment are more cooperative, compete to be environmental, and benefit from reputation
Bibliographic record
Abstract
Protecting the environment is a social dilemma: environmental protection benefits everyone but is individually costly. We propose that protecting the environment is similar to other types of cooperation, in that environmentalism functions as a signal of one’s willingness to cooperate with others. We test several novel predictions from this hypothesis. We used a mathematical model to show that environmentalism can indicate one’s valuation of others and thus one’s cooperative intent. We found support for this prediction in two online studies, and then conducted two laboratory studies to extend the idea that environmentalism signals one’s willingness to cooperate. Participants donated more to an environmental charity when donations were public than when anonymous, but they donated the most when competing to be chosen by an observer for a subsequent cooperative game. In other words, people competed to donate more to the environment. Bigger donors benefited, as they were subsequently chosen more often and received more cooperation from their partners. Partners benefited from choosing environmental donors: bigger donors cooperated more with subsequent partners, such that environmental donations were reliably informative about participants’ future cooperativeness. We compare multiple theories about why people behave environmentally (indirect reciprocity, signal of wealth, signal of cooperative intent), and find most support for our proposed theory of signaling cooperative intent. By understanding the function of environmental behaviour and stimulating competitive giving, we can increase people’s support for environmental and other charitable causes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".